The present invention relates to spread spectrum communication systems generally and to noise reducing units in mobile handsets of such communication systems in particular.
A conventional spread spectrum signal can be viewed as the result of mixing a narrowband information-bearing signal i[t] with an informationless wideband “spreading” signal p[t] . If Bi and Bp denote the bandwidths of i[t] and p[t], respectively, then the “processing gain” available to the receiver is Bp/Bi. The receiver synchronizes the incoming signal to a locally generated version p0[t] of p[t] and mixes the received signal with p0[t], thereby removing p[t] from the signal and “collapsing” the signal to the “information bandwidth” Bi.
The spreading signal p[t] is typically a coding sequence of some kind, such as a pseudo-random code. In many code division multiple access (CDMA) systems, the code is an M-sequence which has good “noise like” properties yet is very simple to construct.
For example, in the IS-95 standard for cellular communication, the forward channel (base to mobile units) employs, as a spreading code, the product of a 64 chip Walsh code (aimed at separating up to 64 different users per base) and a periodic pseudorandom noise (PN) sequence (aimed at separating the different bases). See TIA/EIA IS-95A “Mobile System-Base Station Compatibility Standard for Dual-Mode Wideband Spread Spectrum Cellular System” Telecommunication Industry Association. Thus, the spreading signal p[t] for each user is its Walsh code combined with the current 64 chips of the PN sequence of its base station.
In order to synchronize the local version p0[t] of the spreading signal with the original version p[t], the base station additionally transmits the current PN sequence via a pilot signal z[t] (the pilot signal z[t] is simply the current PN sequence multiplied by the all one Walsh code). The mobile unit then synchronizes its local code generator to the pilot signal after which the mobile unit can despread the received information bearing signals using its Walsh code and the current PN sequence.
The Walsh codes Wi, I=1, . . . 64 are perfectly orthogonal to each other such that, in a non-dispersive transmission channel, there will be complete separation among the users even despite being transmitted at the same time and on the same transmission frequencies.
Practical channels, however, are time dispersive, resulting in multipath effects where the receiver picks up many echoes of the transmitted signal each having different and randomly varying delays and amplitudes. In such a scenario, the code's orthogonality is destroyed and the users are no longer separated. Consequently, a mobile unit, when attempting to detect only a single user, regards all other channel users (including signals from other base stations) as creators of interference. This contributes to a decrease in signal-to-noise ratio (SNR) and thus, reduces the reception quality of the mobile unit.
In the presence of multipath channels, the mobile units additionally process the informationless pilot signal to identify and track the multipath parameters of the channel. For this purpose, the mobile units include a channel estimator which detects and tracks the attenuation, denoted by channel “tap” ĥi, and the relative delay, denoted by {circumflex over (τ)}i, for each of the main paths. The mobile units then utilize the channel information in their detection operations.
In pilot interference cancellation, estimates of interference from co-channel pilot signals from one or more base stations are formed at the receiver and subtracted out from the received signal in order to improve the detection of the desired signal or even multiple desired signals. Since each receiver sees less effective interference, it may need less transmitted power from the base station to obtain its desired block error rate. This transmit power savings may be used to support more users, or to provide higher data rates. The overall reduction in interference can also provide other benefits, such as increasing coverage area.
Pilot channel interference cancellation is particularly attractive because of its low implementation complexity. The information content and structure of pilot channels are known a priori, which makes accurate estimation and generation of interference terms relatively simple. However, it would be desirable to further reduce the computational complexity of pilot channel interference cancellation techniques.
Thus, there is a need for even less complex techniques for canceling pilot channel interference.